classifly: Explore classification models in high dimensions

Given $p$-dimensional training data containing $d$ groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. Generally the input to these algorithms is high dimensional, and the boundaries between groups will be high dimensional and perhaps curvilinear or multi-faceted. This package implements methods for understanding the division of space between the groups.

Version: 0.4
Imports: class, stats, plyr
Suggests: e1071, rggobi, rpart, MASS
Published: 2014-04-23
Author: Hadley Wickham
Maintainer: Hadley Wickham <h.wickham at gmail.com>
License: MIT + file LICENSE
URL: http://had.co.nz/classifly
NeedsCompilation: no
Citation: NA
Materials: NA
CRAN checks: classifly results

Downloads:

Reference manual: classifly.pdf
Package source: classifly_0.4.tar.gz
Windows binaries: r-devel: classifly_0.4.zip, r-release: classifly_0.4.zip, r-oldrel: classifly_0.4.zip
OS X Snow Leopard binaries: r-release: classifly_0.4.tgz, r-oldrel: classifly_0.4.tgz
OS X Mavericks binaries: r-release: classifly_0.4.tgz
Old sources: classifly archive

Reverse dependencies:

Reverse suggests: upclass